{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical Statistics for Data Scientists (Python)\n",
    "# Chapter 1. Exploratory Data Analysis\n",
    "> (c) 2019 Peter C. Bruce, Andrew Bruce, Peter Gedeck"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import required Python packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:32.294296Z",
     "iopub.status.busy": "2022-04-26T19:41:32.293278Z",
     "iopub.status.idle": "2022-04-26T19:41:34.654458Z",
     "shell.execute_reply": "2022-04-26T19:41:34.653515Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import trim_mean\n",
    "from statsmodels import robust\n",
    "import wquantiles\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:34.658338Z",
     "iopub.status.busy": "2022-04-26T19:41:34.658135Z",
     "iopub.status.idle": "2022-04-26T19:41:35.666930Z",
     "shell.execute_reply": "2022-04-26T19:41:35.666004Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    import common\n",
    "    DATA = common.dataDirectory()\n",
    "except ImportError:\n",
    "    DATA = Path().resolve() / 'data'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define paths to data sets. If you don't keep your data in the same directory as the code, adapt the path names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:35.671310Z",
     "iopub.status.busy": "2022-04-26T19:41:35.671080Z",
     "iopub.status.idle": "2022-04-26T19:41:35.675307Z",
     "shell.execute_reply": "2022-04-26T19:41:35.674572Z"
    }
   },
   "outputs": [],
   "source": [
    "AIRLINE_STATS_CSV = DATA / 'airline_stats.csv'\n",
    "KC_TAX_CSV = DATA / 'kc_tax.csv.gz'\n",
    "LC_LOANS_CSV = DATA / 'lc_loans.csv'\n",
    "AIRPORT_DELAYS_CSV = DATA / 'dfw_airline.csv'\n",
    "SP500_DATA_CSV = DATA / 'sp500_data.csv.gz'\n",
    "SP500_SECTORS_CSV = DATA / 'sp500_sectors.csv'\n",
    "STATE_CSV = DATA / 'state.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimates of Location\n",
    "## Example: Location Estimates of Population and Murder Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:35.678420Z",
     "iopub.status.busy": "2022-04-26T19:41:35.678202Z",
     "iopub.status.idle": "2022-04-26T19:41:35.691453Z",
     "shell.execute_reply": "2022-04-26T19:41:35.690552Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         State  Population  Murder.Rate Abbreviation\n",
      "0      Alabama     4779736          5.7           AL\n",
      "1       Alaska      710231          5.6           AK\n",
      "2      Arizona     6392017          4.7           AZ\n",
      "3     Arkansas     2915918          5.6           AR\n",
      "4   California    37253956          4.4           CA\n",
      "5     Colorado     5029196          2.8           CO\n",
      "6  Connecticut     3574097          2.4           CT\n",
      "7     Delaware      897934          5.8           DE\n"
     ]
    }
   ],
   "source": [
    "# Table 1-2\n",
    "state = pd.read_csv(STATE_CSV)\n",
    "print(state.head(8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the mean, trimmed mean, and median for Population. For `mean` and `median` we can use the _pandas_ methods of the data frame. The trimmed mean requires the `trim_mean` function in _scipy.stats_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:35.720234Z",
     "iopub.status.busy": "2022-04-26T19:41:35.719289Z",
     "iopub.status.idle": "2022-04-26T19:41:35.728249Z",
     "shell.execute_reply": "2022-04-26T19:41:35.727173Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6162876.3\n"
     ]
    }
   ],
   "source": [
    "state = pd.read_csv(STATE_CSV)\n",
    "print(state['Population'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:35.731743Z",
     "iopub.status.busy": "2022-04-26T19:41:35.731507Z",
     "iopub.status.idle": "2022-04-26T19:41:35.736311Z",
     "shell.execute_reply": "2022-04-26T19:41:35.735324Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4783697.125\n"
     ]
    }
   ],
   "source": [
    "print(trim_mean(state['Population'], 0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:35.739570Z",
     "iopub.status.busy": "2022-04-26T19:41:35.738914Z",
     "iopub.status.idle": "2022-04-26T19:41:35.743616Z",
     "shell.execute_reply": "2022-04-26T19:41:35.742791Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4436369.5\n"
     ]
    }
   ],
   "source": [
    "print(state['Population'].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weighted mean is available with numpy. For weighted median, we can use the specialised package `wquantiles` (https://pypi.org/project/wquantiles/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:35.747867Z",
     "iopub.status.busy": "2022-04-26T19:41:35.746718Z",
     "iopub.status.idle": "2022-04-26T19:41:35.751766Z",
     "shell.execute_reply": "2022-04-26T19:41:35.750725Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.066\n"
     ]
    }
   ],
   "source": [
    "print(state['Murder.Rate'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:35.754703Z",
     "iopub.status.busy": "2022-04-26T19:41:35.754436Z",
     "iopub.status.idle": "2022-04-26T19:41:35.758428Z",
     "shell.execute_reply": "2022-04-26T19:41:35.757718Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.445833981123393\n"
     ]
    }
   ],
   "source": [
    "print(np.average(state['Murder.Rate'], weights=state['Population']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:35.762385Z",
     "iopub.status.busy": "2022-04-26T19:41:35.761389Z",
     "iopub.status.idle": "2022-04-26T19:41:35.767182Z",
     "shell.execute_reply": "2022-04-26T19:41:35.766512Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.4\n"
     ]
    }
   ],
   "source": [
    "print(wquantiles.median(state['Murder.Rate'], weights=state['Population']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimates of Variability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:35.770317Z",
     "iopub.status.busy": "2022-04-26T19:41:35.770083Z",
     "iopub.status.idle": "2022-04-26T19:41:35.776432Z",
     "shell.execute_reply": "2022-04-26T19:41:35.775632Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         State  Population  Murder.Rate Abbreviation\n",
      "0      Alabama     4779736          5.7           AL\n",
      "1       Alaska      710231          5.6           AK\n",
      "2      Arizona     6392017          4.7           AZ\n",
      "3     Arkansas     2915918          5.6           AR\n",
      "4   California    37253956          4.4           CA\n",
      "5     Colorado     5029196          2.8           CO\n",
      "6  Connecticut     3574097          2.4           CT\n",
      "7     Delaware      897934          5.8           DE\n"
     ]
    }
   ],
   "source": [
    "# Table 1-2\n",
    "print(state.head(8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:35.779557Z",
     "iopub.status.busy": "2022-04-26T19:41:35.779301Z",
     "iopub.status.idle": "2022-04-26T19:41:35.784171Z",
     "shell.execute_reply": "2022-04-26T19:41:35.783323Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6848235.347401142\n"
     ]
    }
   ],
   "source": [
    "print(state['Population'].std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interquartile range is calculated as the difference of the 75% and 25% quantile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:35.787566Z",
     "iopub.status.busy": "2022-04-26T19:41:35.787016Z",
     "iopub.status.idle": "2022-04-26T19:41:35.792405Z",
     "shell.execute_reply": "2022-04-26T19:41:35.791742Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4847308.0\n"
     ]
    }
   ],
   "source": [
    "print(state['Population'].quantile(0.75) - state['Population'].quantile(0.25))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Median absolute deviation from the median can be calculated with a method in _statsmodels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:35.795428Z",
     "iopub.status.busy": "2022-04-26T19:41:35.795210Z",
     "iopub.status.idle": "2022-04-26T19:41:35.801633Z",
     "shell.execute_reply": "2022-04-26T19:41:35.800953Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3849876.1459979336\n",
      "3849876.1459979336\n"
     ]
    }
   ],
   "source": [
    "print(robust.scale.mad(state['Population']))\n",
    "print(abs(state['Population'] - state['Population'].median()).median() / 0.6744897501960817)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Percentiles and Boxplots\n",
    "_Pandas_ has the `quantile` method for data frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:35.805389Z",
     "iopub.status.busy": "2022-04-26T19:41:35.804986Z",
     "iopub.status.idle": "2022-04-26T19:41:35.813654Z",
     "shell.execute_reply": "2022-04-26T19:41:35.812743Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05    1.600\n",
      "0.25    2.425\n",
      "0.50    4.000\n",
      "0.75    5.550\n",
      "0.95    6.510\n",
      "Name: Murder.Rate, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(state['Murder.Rate'].quantile([0.05, 0.25, 0.5, 0.75, 0.95]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:35.816851Z",
     "iopub.status.busy": "2022-04-26T19:41:35.816645Z",
     "iopub.status.idle": "2022-04-26T19:41:35.825931Z",
     "shell.execute_reply": "2022-04-26T19:41:35.825018Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             5.0%  25.0%  50.0%  75.0%  95.0%\n",
      "Murder.Rate   1.6  2.425    4.0   5.55   6.51\n"
     ]
    }
   ],
   "source": [
    "# Table 1.4\n",
    "percentages = [0.05, 0.25, 0.5, 0.75, 0.95]\n",
    "df = pd.DataFrame(state['Murder.Rate'].quantile(percentages))\n",
    "df.index = [f'{p * 100}%' for p in percentages]\n",
    "print(df.transpose())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Pandas_ provides a number of basic exploratory plots; one of them are boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:35.829031Z",
     "iopub.status.busy": "2022-04-26T19:41:35.828825Z",
     "iopub.status.idle": "2022-04-26T19:41:36.009799Z",
     "shell.execute_reply": "2022-04-26T19:41:36.008741Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (state['Population']/1_000_000).plot.box(figsize=(3, 4))\n",
    "ax.set_ylabel('Population (millions)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency Table and Histograms\n",
    "The `cut` method for _pandas_ data splits the dataset into bins. There are a number of arguments for the method. The following code creates equal sized bins. The method `value_counts` returns a frequency table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:36.013220Z",
     "iopub.status.busy": "2022-04-26T19:41:36.013014Z",
     "iopub.status.idle": "2022-04-26T19:41:36.024317Z",
     "shell.execute_reply": "2022-04-26T19:41:36.023625Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(526935.67, 4232659.0]      24\n",
      "(4232659.0, 7901692.0]      14\n",
      "(7901692.0, 11570725.0]      6\n",
      "(11570725.0, 15239758.0]     2\n",
      "(15239758.0, 18908791.0]     1\n",
      "(18908791.0, 22577824.0]     1\n",
      "(22577824.0, 26246857.0]     1\n",
      "(33584923.0, 37253956.0]     1\n",
      "(26246857.0, 29915890.0]     0\n",
      "(29915890.0, 33584923.0]     0\n",
      "Name: Population, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "binnedPopulation = pd.cut(state['Population'], 10)\n",
    "print(binnedPopulation.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:36.027414Z",
     "iopub.status.busy": "2022-04-26T19:41:36.027172Z",
     "iopub.status.idle": "2022-04-26T19:41:36.043921Z",
     "shell.execute_reply": "2022-04-26T19:41:36.043149Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   BinRange  Count  \\\n",
      "0    (526935.67, 4232659.0]     24   \n",
      "1    (4232659.0, 7901692.0]     14   \n",
      "2   (7901692.0, 11570725.0]      6   \n",
      "3  (11570725.0, 15239758.0]      2   \n",
      "4  (15239758.0, 18908791.0]      1   \n",
      "5  (18908791.0, 22577824.0]      1   \n",
      "6  (22577824.0, 26246857.0]      1   \n",
      "7  (26246857.0, 29915890.0]      0   \n",
      "8  (29915890.0, 33584923.0]      0   \n",
      "9  (33584923.0, 37253956.0]      1   \n",
      "\n",
      "                                              States  \n",
      "0  WY,VT,ND,AK,SD,DE,MT,RI,NH,ME,HI,ID,NE,WV,NM,N...  \n",
      "1          KY,LA,SC,AL,CO,MN,WI,MD,MO,TN,AZ,IN,MA,WA  \n",
      "2                                  VA,NJ,NC,GA,MI,OH  \n",
      "3                                              PA,IL  \n",
      "4                                                 FL  \n",
      "5                                                 NY  \n",
      "6                                                 TX  \n",
      "7                                                     \n",
      "8                                                     \n",
      "9                                                 CA  \n"
     ]
    }
   ],
   "source": [
    "# Table 1.5\n",
    "binnedPopulation.name = 'binnedPopulation'\n",
    "df = pd.concat([state, binnedPopulation], axis=1)\n",
    "df = df.sort_values(by='Population')\n",
    "\n",
    "groups = []\n",
    "for group, subset in df.groupby(by='binnedPopulation'):\n",
    "    groups.append({\n",
    "        'BinRange': group,\n",
    "        'Count': len(subset),\n",
    "        'States': ','.join(subset.Abbreviation)\n",
    "    })\n",
    "print(pd.DataFrame(groups))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Pandas_ also supports histograms for exploratory data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:36.047730Z",
     "iopub.status.busy": "2022-04-26T19:41:36.047476Z",
     "iopub.status.idle": "2022-04-26T19:41:36.184054Z",
     "shell.execute_reply": "2022-04-26T19:41:36.183237Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (state['Population'] / 1_000_000).plot.hist(figsize=(4, 4))\n",
    "ax.set_xlabel('Population (millions)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Density Estimates\n",
    "Density is an alternative to histograms that can provide more insight into the distribution of the data points. Use the argument `bw_method` to control the smoothness of the density curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:36.202105Z",
     "iopub.status.busy": "2022-04-26T19:41:36.201867Z",
     "iopub.status.idle": "2022-04-26T19:41:36.366117Z",
     "shell.execute_reply": "2022-04-26T19:41:36.365240Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = state['Murder.Rate'].plot.hist(density=True, xlim=[0, 12], \n",
    "                                    bins=range(1,12), figsize=(4, 4))\n",
    "state['Murder.Rate'].plot.density(ax=ax)\n",
    "ax.set_xlabel('Murder Rate (per 100,000)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Binary and Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:36.369880Z",
     "iopub.status.busy": "2022-04-26T19:41:36.369186Z",
     "iopub.status.idle": "2022-04-26T19:41:36.379958Z",
     "shell.execute_reply": "2022-04-26T19:41:36.379056Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Carrier        ATC   Weather  Security    Inbound\n",
      "0  23.022989  30.400781  4.025214  0.122937  42.428079\n"
     ]
    }
   ],
   "source": [
    "# Table 1-6\n",
    "dfw = pd.read_csv(AIRPORT_DELAYS_CSV)\n",
    "print(100 * dfw / dfw.values.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Pandas_ also supports bar charts for displaying a single categorical variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:36.383643Z",
     "iopub.status.busy": "2022-04-26T19:41:36.383383Z",
     "iopub.status.idle": "2022-04-26T19:41:36.498392Z",
     "shell.execute_reply": "2022-04-26T19:41:36.497651Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = dfw.transpose().plot.bar(figsize=(4, 4), legend=False)\n",
    "ax.set_xlabel('Cause of delay')\n",
    "ax.set_ylabel('Count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation\n",
    "First read the required datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:36.501519Z",
     "iopub.status.busy": "2022-04-26T19:41:36.501289Z",
     "iopub.status.idle": "2022-04-26T19:41:37.559617Z",
     "shell.execute_reply": "2022-04-26T19:41:37.558569Z"
    }
   },
   "outputs": [],
   "source": [
    "sp500_sym = pd.read_csv(SP500_SECTORS_CSV)\n",
    "sp500_px = pd.read_csv(SP500_DATA_CSV, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:37.563195Z",
     "iopub.status.busy": "2022-04-26T19:41:37.563002Z",
     "iopub.status.idle": "2022-04-26T19:41:37.573188Z",
     "shell.execute_reply": "2022-04-26T19:41:37.572360Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   T       CTL       FTR        VZ      LVLT\n",
      "2012-07-02  0.422496  0.140847  0.070879  0.554180 -0.519998\n",
      "2012-07-03 -0.177448  0.066280  0.070879 -0.025976 -0.049999\n",
      "2012-07-05 -0.160548 -0.132563  0.055128 -0.051956 -0.180000\n",
      "2012-07-06  0.342205  0.132563  0.007875  0.140106 -0.359999\n",
      "2012-07-09  0.136883  0.124279 -0.023626  0.253943  0.180000\n",
      "...              ...       ...       ...       ...       ...\n",
      "2015-06-25  0.049342 -1.600000 -0.040000 -0.187790 -0.330002\n",
      "2015-06-26 -0.256586  0.039999 -0.070000  0.029650 -0.739998\n",
      "2015-06-29 -0.098685 -0.559999 -0.060000 -0.504063 -1.360000\n",
      "2015-06-30 -0.503298 -0.420000 -0.070000 -0.523829  0.199997\n",
      "2015-07-01 -0.019737  0.080000 -0.050000  0.355811  0.139999\n",
      "\n",
      "[754 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "# Table 1-7\n",
    "# Determine telecommunications symbols\n",
    "telecomSymbols = sp500_sym[sp500_sym['sector'] == 'telecommunications_services']['symbol']\n",
    "\n",
    "# Filter data for dates July 2012 through June 2015\n",
    "telecom = sp500_px.loc[sp500_px.index >= '2012-07-01', telecomSymbols]\n",
    "telecom.corr()\n",
    "print(telecom)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we focus on funds traded on major exchanges (sector == 'etf'). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:37.576401Z",
     "iopub.status.busy": "2022-04-26T19:41:37.576201Z",
     "iopub.status.idle": "2022-04-26T19:41:37.588251Z",
     "shell.execute_reply": "2022-04-26T19:41:37.587426Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 XLI       QQQ       SPY       DIA       GLD    VXX       USO  \\\n",
      "2012-07-02 -0.376098  0.096313  0.028223 -0.242796  0.419998 -10.40  0.000000   \n",
      "2012-07-03  0.376099  0.481576  0.874936  0.728405  0.490006  -3.52  0.250000   \n",
      "2012-07-05  0.150440  0.096313 -0.103487  0.149420  0.239991   6.56 -0.070000   \n",
      "2012-07-06 -0.141040 -0.491201  0.018819 -0.205449 -0.519989  -8.80 -0.180000   \n",
      "2012-07-09  0.244465 -0.048160 -0.056445 -0.168094  0.429992  -0.48  0.459999   \n",
      "\n",
      "                 IWM       XLE       XLY       XLU       XLB       XTL  \\\n",
      "2012-07-02  0.534641  0.028186  0.095759  0.098311 -0.093713  0.019076   \n",
      "2012-07-03  0.926067  0.995942  0.000000 -0.044686  0.337373  0.000000   \n",
      "2012-07-05 -0.171848 -0.460387  0.306431 -0.151938  0.103086  0.019072   \n",
      "2012-07-06 -0.229128  0.206706  0.153214  0.080437  0.018744 -0.429213   \n",
      "2012-07-09 -0.190939 -0.234892 -0.201098 -0.035751 -0.168687  0.000000   \n",
      "\n",
      "                 XLV       XLP       XLF       XLK  \n",
      "2012-07-02 -0.009529  0.313499  0.018999  0.075668  \n",
      "2012-07-03  0.000000  0.129087  0.104492  0.236462  \n",
      "2012-07-05 -0.142955 -0.073766 -0.142490  0.066211  \n",
      "2012-07-06 -0.095304  0.119865  0.066495 -0.227003  \n",
      "2012-07-09  0.352630 -0.064548  0.018999  0.009457  \n"
     ]
    }
   ],
   "source": [
    "etfs = sp500_px.loc[sp500_px.index > '2012-07-01', \n",
    "                    sp500_sym[sp500_sym['sector'] == 'etf']['symbol']]\n",
    "print(etfs.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to the large number of columns in this table, looking at the correlation matrix is cumbersome and it's more convenient to plot the correlation as a heatmap. The _seaborn_ package provides a convenient implementation for heatmaps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:37.591316Z",
     "iopub.status.busy": "2022-04-26T19:41:37.591125Z",
     "iopub.status.idle": "2022-04-26T19:41:37.859238Z",
     "shell.execute_reply": "2022-04-26T19:41:37.858610Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 4))\n",
    "ax = sns.heatmap(etfs.corr(), vmin=-1, vmax=1, \n",
    "                 cmap=sns.diverging_palette(20, 220, as_cmap=True),\n",
    "                 ax=ax)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above heatmap works when you have color. For the greyscale images, as used in the book, we need to visualize the direction as well. The following code shows the strength of the correlation using ellipses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:37.862840Z",
     "iopub.status.busy": "2022-04-26T19:41:37.862681Z",
     "iopub.status.idle": "2022-04-26T19:41:38.085375Z",
     "shell.execute_reply": "2022-04-26T19:41:38.084653Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.collections import EllipseCollection\n",
    "from matplotlib.colors import Normalize\n",
    "\n",
    "def plot_corr_ellipses(data, figsize=None, **kwargs):\n",
    "    ''' https://stackoverflow.com/a/34558488 '''\n",
    "    M = np.array(data)\n",
    "    if not M.ndim == 2:\n",
    "        raise ValueError('data must be a 2D array')\n",
    "    fig, ax = plt.subplots(1, 1, figsize=figsize, subplot_kw={'aspect':'equal'})\n",
    "    ax.set_xlim(-0.5, M.shape[1] - 0.5)\n",
    "    ax.set_ylim(-0.5, M.shape[0] - 0.5)\n",
    "    ax.invert_yaxis()\n",
    "\n",
    "    # xy locations of each ellipse center\n",
    "    xy = np.indices(M.shape)[::-1].reshape(2, -1).T\n",
    "\n",
    "    # set the relative sizes of the major/minor axes according to the strength of\n",
    "    # the positive/negative correlation\n",
    "    w = np.ones_like(M).ravel() + 0.01\n",
    "    h = 1 - np.abs(M).ravel() - 0.01\n",
    "    a = 45 * np.sign(M).ravel()\n",
    "\n",
    "    ec = EllipseCollection(widths=w, heights=h, angles=a, units='x', offsets=xy,\n",
    "                           norm=Normalize(vmin=-1, vmax=1),\n",
    "                           transOffset=ax.transData, array=M.ravel(), **kwargs)\n",
    "    ax.add_collection(ec)\n",
    "\n",
    "    # if data is a DataFrame, use the row/column names as tick labels\n",
    "    if isinstance(data, pd.DataFrame):\n",
    "        ax.set_xticks(np.arange(M.shape[1]))\n",
    "        ax.set_xticklabels(data.columns, rotation=90)\n",
    "        ax.set_yticks(np.arange(M.shape[0]))\n",
    "        ax.set_yticklabels(data.index)\n",
    "\n",
    "    return ec, ax\n",
    "\n",
    "m, ax = plot_corr_ellipses(etfs.corr(), figsize=(5, 4), cmap='bwr_r')\n",
    "cb = fig.colorbar(m, ax=ax)\n",
    "cb.set_label('Correlation coefficient')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatterplots\n",
    "Simple scatterplots are supported by _pandas_. Specifying the marker as `$\\u25EF$` uses an open circle for each point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:38.088751Z",
     "iopub.status.busy": "2022-04-26T19:41:38.088496Z",
     "iopub.status.idle": "2022-04-26T19:41:38.360367Z",
     "shell.execute_reply": "2022-04-26T19:41:38.359445Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4AHThsmZ0RzSrWR6AYpg1dmPUuOf173ThkjHmFjRPUALePhzh8vjUqFDGTbgAwKTVWaYu6Jyzl/D47Y1RplRJH9+Cb+wspRCpsgzeiKSA2QSgCRE1IKLSAAYCWG0cQETGMMs+AHaHcX4xjVMZtIH8gH0JuWBbe3irajekXT30mvM1stV4lbHLt4MZaKEWm9o8sSv2Te+BlY+2A6DEy5w8dwm7cpRau3e97irq7bnEWjjMvU7v3MGp+HHC7fr2y5/vC1qI2/kgiJaHipGICRhmvgJgFICPoQiOD5g5i4imElEfddgYIsoiom0AxgAYGpnZxhZOBWcF+gO2IuR8CSyzz+89oQgQzbWt8e53ylwaGbw1PWd/haycPDzdvRmOnLmAxhPXou+rioF29JIteoazP+5/e5Pb9qhFW9B2hrsROLFiGUxYsROnzl2CGd6u084HQSTLMngjogtzZs5g5qbM3IiZn1f3TWLm1err8czcgplbMXNnZt7j+4gC4FxwViA/YCtCzpfA8vz8xN5K+cpu//5SH6NpGqM6N9L3aYWnAPeSl33mKsFzHzzsWgqZ8Y6HtuKPCnHK0ujoWWVJ1Xr6Z4UEr7frtPNBEK0Rv9Fj+RNsxe7grEB/wFaEnC+BZfz8l9m5GPimsoQxupk/eeJWAMCTdybpxzQWntJKXhq5943vC+0zMmV1ls/3Pcm/dNVt26zfkrfrtPNBEK0Rv5YEDBGlEdETRPSSuoS5l4gqOz05IXgCMaRaIZgfsC8hZ0VgvTr4RgDA/WpMS1q9SmhTvwreeUDJNTp7wXUTa+fSSihMS28e1HUePPVHUJ8DgAk9k/D+31wJkID7dSbVqoCX1Pwm7X07HwTRGPHr001NRMMAjAbwC4DNAE4AKAOgKYAOAHYCeJaZf3N+qtYRN7UzBONe9nSVG7ebTlwLAFg4rC1eWLcbu3Py3dy99cetUd5/oA3uX6DYQWb1vwFPLnflCA0rm4lnn52Eb/afdMtB8oQAhCMgw8xtrV2HkUm9k/FAx4YA7A0nCFdoglU3tT8BMxLAAmY2TaQgohQAVZn5c7P3I4UIGOcI5QecdfQses12dQMY1LYuFv/oiptMT0nAKwMVrYWZ0WB8hl6PZV1WDh5931VYe3SXRtiwJxdtTq/H2xf8/s5DZsWj7XHuwhXcZ4gQrlGhNE7kFw7OMwoPoLBgLgqxM7bEwTDzPB/CJY6Zt0abcBGcJdiQ9d05eW7CJaFinC5cFj7QBi0S47Fqa46pDabhhAw34TLlL80xuktTt+xrp1m385ibcJma3hxrH1NtQN2a4cCMnlj7mJIicGO9KgBcnqOG1ZRl5LS+LfDz9B5YM0b5XKQNsOHA56+EiHLUZZIZhVviCYIJxjKW43o0Q8vEeOScVdy5T3Zrhlub1sB7JjYYz0A5LcN5yn93oenEtej5iuJR+ufd1zsy7yfvaKK/fuPLA27vTVq1C3+dr9ihZn2SjR1HzurlHbR6vJrnqNmz6wAAz67Mwr4T59zsV9EWGGc3/h5DFwEMJaJlRFTR4z0y+4AgGNFuNI2Za7PxTC+XAXbWJ9lejcZJalnJaX1bYPWoDjjpkSt0LE8RUk/9Z4cjc5/16V637TqV3JMi9xzL119rxmXPeryerU2MBtgjpy9EXWCc3fgTMKcBdAKwDcAWIupkeC8ySUxCTGEWXj9ILWM5/NYGAMy9HgUFjGN5SmzJsyuz9DiWSFC3slLd/7CaPtCg6rVu7yspCoow7N0q0auHbPkj7bBqZHvsnHInBt9ULyoD4+zG70KaFaZDCeV/g4hmEpEUCy/GWFXrjTea53Jnap8WmNCzuakr3bi8MKNT0+pu2y/edT2+GdcZG/5+GwDgsS6KgfWJrsoS59HbGsEXT3Zrij1Tu+Obpztj/T9uxdKHFYHw8oAUAIWLRf1y6g8837cFAEW7alKzglt9Xm8u/f6vfYf0ed+i5ZSP8f73v+rfTTQFxtmNPy/SFmZONWyXBzAbwA0AqjNzePpPBoh4kZwjUFe15qL9aHRHPLF0C/aeOI/NE7uiavk4FBQwjpz9A8fPXkRSrYq4tnRJHM+/qAsWrfr/TkNKgCfDymYiPzkdy3864nWMFX56tiuW/HgI//w4223/vMGpek1dI6O7NHbL4NYKS2meIc/vScPsmjLG3IKxy7ch62hezHiW7HJTL2DmB0z29wcwnZmTTD4WcUTAOIcmMIw3iq+bwuxGm9GvJVKvq2x6A2oMbFMXM+++Ab+fv4wbp32Klwek6E3pjQwrmxm0m3r2wFaYmbEHR/Pc84daX1cJ2w+fwZ8mCpovofds72QMa9/ArX3J8fyLqHJtaTR7dh3mDk5F7xsSvV5TOHtGh4pd5RrWElEZz53MvDxahYvgHMHkuyQnuAycWuHsCSt2ugmXOpVdP7F3hiq5QEs2HcKmg7/ry4vOScqy6B/dlGXPyE7uy574Mu7lEu5IruH3esYs2VZIuADA5t9cwqVTs2rYMbkbAGU51LhGebyrRhL/9aa62D6pm778q1ouzs1ou2TTb0ioWBZx1yhz05ZM/zNfsUF1aFxVrxG8Z2p3dE6qUeS8Sf40mBVQInY/BrAYwMfMfNXrB6IE0WACI5DgOaMGY0WtNwbMGZ/eGgPS6uCJO5riZnVZNKFnEpIT4t2icn1pPKFoML4oQUCBl1ujUtlSOHPhir4dX6Yk8i66bgsz7c7bksmMWNBkbFkiqQeKB9APipE3BcAqAIuZ+Qsb5ukIImCsE6hNJZh0ATOhZIVPnrgV9auWQ+lSJUzD7QHnBEygPHlHE8z6dC9eHtAKfVPr6IJ05cgOeinNggJ2c9n7ItptMbZVtGPmPGZ+l5l7AGgJYAuA2URkWhtXiC0CdZUGk0RploRnhVoVy6B0qRJuTdWihevVurtLhivLRC1m5vGl2zDr42zTuB6tq8mcQcp1zLzLPUDw3wNS9NifHYfPOjr/cGE53lvNnr4LwAAAVQAsd2pSQngIVw0RT6E0qK1SlH3OwFR88dRtmKVG4v6jWxN8N74LflCrxWnzMDZVCzejOjd229Za0O44chb/+ekwhi5wFaPSWsTO3bAPWUfzMDW9uZsWol/HYuU6xn3oHiBYp1LZQs3bYh2f8SyqW7ofgEEAUqGUtJwGYCMXtW+iGOKtwr+vEgzBFuzWcpiMeAqMf32yF63qVMLMtdn6PAoKGDl5wTctC5W5G9yLif9y8g+k1I3H1kN5GGvI6p47OBW9bkhElWtLY5CaQjBp1S6UKlECA9tcp9u4tL5JZtzzhiv75vo6noHzsYk/I+9JAOug9Cz6mJljIsxQbDDWCTauxaqb2hsZ23Pw6KKf9O2y15TABYNfuE+rWujf+rpCZS2Hta+Pt7896Np2yAbTNyURK7ceLbS/UfVyXjsSZIy5Ra+yt2xEOzy3OquQO3tGv5a6wKlctjROX7iM3PxLbpHKxcbIS0RlvWVTRzMiYALDqhfJm0fI05Bp5ViaoHq0UyNszD6hF9+e3Ls53vv+IA6c9F74qV7lMvj1tBK2H0kjr7eYGM8YHs+xZgI5WlvMeMMuI+9SIupNRIUeT0TUUK1uVygQT4gtrJZg8FfVzrP27MJvD+LKlQJsO3Qa5y663LrGh9qrG/fj2d6u5MeOTar5FC4AdOHiFD1a1rQ0buuh06YlI5ZsOhRwr+1Y7dzoD38aTC0AfwdwN4DfAeRCqWhXH8B+AHOZeZXz0wwM0WCcw9eSypsrWUMrKBWIu9YfkXZTJ9WqoGdVG3ttexKL6QC+sKrB+DTyMvMxAE8BeIqI6gNIAHABwM/MHHzxUiFm8dba1fNB9d4DbXGfaj95qf8NeHblTqzamoO7bjyBIQs2FTquGZ2bVcOG7JP2XkCIvNCvJVIMQX+acDH22r5x2qeYPyQNtzapjv2559Djla9020w01MkNJ5b1MWY+yMzfqVXsRLgUcXxlTJup89rySeM+g3G2Sc0KWKPGwvgTLmPvbIqnujcDgKgTLgAwfsVOHD1zAclqvMrmiV0BFF42evbaDrT4elEpRCVlF4RChNo72pO9x/Px7Mqdlsa+9PHPQZ/HCdaM7ogxS7a4eY4efFdZfqenJKBqeZfr2ZuWYuai94Xd338kKVoWJcEWgimE5MuWN3b5dly8UoD0FFcn4KdVLSUaKOWjNmOvOV8XckvXq6IE22k1hO1uEVOUClGJgBHc8Bbdu+/EOV1lN1PfjUukp+9shqZqE3ojSbXisXpUBwBKt0Ujvm5yp7niIRsT4uO8jp07OBUrRnbQt7Xv5UoB2+IFitYOjcFiaYlERB0ATAFQT/0MQSl219DX5ywctzuAVwCUBDCfmWd6vB8HYCGA1gBOARjAzAdDOafgG2/RvXe9+q3peKP6ri0VXvQo2qT1MlKESjZGdW6EuRv2u43xvMmdpFQJwhVvqdIAcgwlHBIrlcHRMy63+KhFW9z6Xxu/FzuWMsFEV0czVsXtWwD+F0BHAG0ApKn/Bg0RlQQwD0APAM0BDCIiz3Z8DwI4zcyNAfwbwIuhnFOwhmdyokbGGFfZy2UPtyukvicnxGPNmI5uxxrYpq5bo7SG1cph7ob92DrpDicvwSe+hItGhTjl2asJl7tvrK2/57lk8reUCdRgG40dGoPFqoA5y8xrmfkEM5/S/kI8d1sA+5j5ADNfhpKOkO4xJh3Au+rr5QBuJ093hWA7RptC9rTuABSVPTmhgj7mmlIlTNX3FokVsW96D6x4tB0AJegMAOqohbM1D9EvJ81V/v+9t5XXeZWLK+n1PTuY0deV5Z1/6Yrbe//56Qgm9kzWC0QZvxdfSxlvje99YbdNJ5L4rQcDAEQ0E8oy5kMAuv7IzD95/ZD/Y/YH0J2ZH1K37wNwEzOPMozZqY45rG7vV8cU8l8S0XAAwwEgISGh9cMPPxzs1ARB8MOUKVNCD7QzoMVDGw/IALoEOjGnYOY3AbwJKJG8kydPjvCMYg9v+TC+qrEZ7Q6e4yqVLYkzF5wtgGhnJO834zqjdiXFQ+QtKtmYd+XPnWwldytWmTJliqVxlgQMM3cOZTJeOAKgrmG7jrrPbMxhtVVKRSjGXsFmfN0sntG7WjFrT0HkKYTMhMuKR9vjiSVbcPD36Muhzc2/rAsYbxiNrd6imjUCMdjGWrKjVSxdCRFVJKL/JaJM9e9fJp0eA2UTgCZE1ICISkMpybnaY8xqAEPU1/0BrJc6NM7gL/bCGL1rFslr9b+l36vfRqVwAVw3vnYt4zxidR65rWEhY6u/JEUrBttg7DSxglVRuQBAPoB71b88AG+HcmJmvgJgFJSC4rsBfMDMWWqGdh912FsAqhLRPihJl+NCOadgjh2xF0XJ9q5dy8x12VgzuqMe0/OIWt0uEK+QFYNtUQqs88SqgGnEzJNVj88BZn4OQEgxMADAzBnM3JSZGzHz8+q+Scy8Wn19kZnvYebGzNyWmQ/4PqIQDP7KMFjFs3sjANSuVKjrTVQxuM11aFpTuU5jHVztWnrN+Ro/Hz+HqX1a6N0BAtU2fGk5RS2wzhOrAuYCEekBDmrgXXTquUJQhBp7UVDAiC9bChv/0QkL7m+t7z9yxtnaLaHSpmEVEBW+DTxjeiatzsKiH361XduwS7hHK1bd1ClQ4lE0u8tpAEOZeZtzUwseqQcTHMEaGgPp+ePJmC6NMXv9Pv8DvWCnF8mzTotZeVAAfr1CgX6PsZjcaEs9GA1m3gqgldojCcxsrbGNEFMEmvWrYbw5fNWsfez2xnjlc3dhEopwsRNPjc1s6aKVv/TlFQpGWPjzRsUyVr1IV9Vgu3xNuBBR0EF2QtHBqAHPHZyKZSPa69v/GdHObawmXBo5oP4vHNbW8ti+hqzu+UPSTI2v3pYuy9Vr8raUDHYJVVRLZloNtMuCIow+IaIBzPw7lIRHoZhj9B6NWrQF9aq4NKC7X//O7CNeNZxg6dSsOm5pWs3y+K2/ndFft21QBaVKEHLOXiikPZjVeUmrX8WrtuFN6zmQe95SYF1RjIWxKmCuMPNTRDQAwFdEdD+USF6hGODvh2/s9fOrR4xLxphbMHbZVmSpXQOcYGN2Lr7bbz3+0hiH88bGfZi30eWc9BVgqF27t6VkKIF1sWiHsYJVMUkAwMxLoXR2fBs2uKmF6MeKWzY5IV5/vWBIGpqr2y/1vwFJtSpg1r0pjs9zsNrszCqrRnZA3cpl3YRLfJmSPgMMrRBsYF1RjYWxKmAe0l4w804AtwAY48iMhIhjDCSz8sM3Lg26JNfE/z2kxHKMXb4dDSdkBO1hshvj8q1B9XI4dFrRZLq3qIm4koS8i0pqQygxKMEG1gFFMxbGX+vYLsy8HkA9IvL8ps45Ny0hUpi5nP3ZFLwtDaIN4/Jt1trd+ut1Wcfdxm3+9feQkhF9eeOC9U7FKv40mNvUf/9i8tfbwXkJEcLz6QpYCwLzXBo8b6itAgBjujRyctqmJHpEEdep7Npe+MMh08/UjI/DtI92O7Y8CdY7Fav464s0mZQwx7XM/EGY5iRECF9PV7OK+UZDpadBdH+uu4K7bLNnorzzHD1zEQ92rI+3vj4IADjs0RHytibV8MVeV2mhVnXi8fawwDw/wRCodyqW8etFYuYCInoKgAiYIo63pc7WSXfgwp9X3X74nkup6ektcXvzGvoYz2VWzllnUwbuu/k6vPf9b4X2r9meo79eMCQNsz75GbtylDjRL/aexMsDWuHxpcp1PnFHs7CE6gfqnYplAqlodxLAUgC65UmNh4k6JFUgeKy4S69cKUDjiWsBmLdLferOpvinj/5G9auWRQkivz2orWAlVWD4rQ3w5pe/6Nsz+rVEpbKl8egi81hRu13ERTG+xWqqgFUB84vJ7pC7CjiFCJjQ8HVDZGzPcbsx01MSsGqroiWM7dYML33i3lHgnta1Q1oe9WmVgNXbcry+byZgureoid9+/wO71NibFY+0R42Kcbh6lVHL4HLeefgMes/9Rv/c092b4cGODW0VAkU1vsVWARNriIBxDn8N7sONUcC8MiAFjy3dqr+XULFMoaWZ8QY3S2b01Zg+GE0k0HPEClYFjNVcpGuJaCIRvaluNyEi8SIVMwoKlAJLD3Sob1r7BXB1DzCjfhVr9oVgclCe7NYU6am18f5Drpwko3DxjOPRHqyzB6Wgcrlr8NZQpQuPt9iTYOrAFPVaL1awqgu+DeAyAC2T7QiA6Y7MSAgZJxqnK09vpaHEgm8OYu/xfMSpT/G3hrjqvxw+7b1MkGepzPpVzevfBqNTz/rkZ6zZnoO/zv9R3/dcn2QA5je4ZtAes3gr2r2wHjfN+ByAd+NuMJG2Rb3WixUCqWj3TwB/AgAz/wFJdoxKnKjvmnX0rH5MjTFLtuKSKsCO513Cvuk98MqAwj2NalUo7fW4B0+FbuQFFCMuAIxUbUOPdmqIlonxmLxaCaYL9QYPRRMpSk3UgsGqgLlMRGWhPlyIqBEM/ZGE6MHunJbdOXnoNftrfTuhoqtv8/IR7dBCPUfjiWvx2NLCEbzH8i/rr301VfNG1XLeBZRGz+sT8dEoV7/oVzceQHpKIgDg5QEpyhiPG1wTGnMGpuK78V3w/fjbAZgLjVA0kaLURC0YrAqYKQDWAahLRO8D+BzAU05NSggOJ9b8msAa16MZWibGI+es8lx5slszlIsr5dZa1ow6hmjav3+wDfMG3xjQ+U+dv4w7m9fwOaZ+1WvdvEGNqpfD8xl7AABdkmuY3uCa0Bi9ZAtOn/8TD767CYD3zOf3HlRsO8FoIkW11osVfF4xEc0jog7M/AmAuwAMBbAYQBozb3R+ekIg2L3mv3rVZcOZuTYbz/RytQ6f9Um2m/u1ZnwczLi+jnt3m5EesSfDOvh/on+864TP91Omfuq2rdWbeaRTI8SXucbtBjfapwLJfL7vLcW283T3ZsVSEwkWn25qInoMSr+iBCiRvIuZeUuY5hY0xdlNbVfcha86uw/f2gBvfGkWGuWbpJrlsee4tRzZupXL4NBp/9G/ZnEwWtlOT3ew5zVN7dMC/3NzPZ+u56LqZg4VW9zUzPwKM7eDkvR4CsACItpDRJOJqKlNcxVsxK41v3Yjei5pnvtLcwzt0CDg483o1xLv/+1my+ONwqVLUnUASk1fIyNuc8V5arYWQNFgzLQRT4E5aXUW3vn2l2LbUiQcWFoUMvOvzPwiM6cCGASgL5RmaUIUEuqa33hj9bohAZkTuwIAZvW/AZP/u8vNm+SLauVcqW4pdSvjnte/DWo+6/fkAkChguG3J9fUX9euVBbNEyoAADZP7FpIsHpq6pqLfKqPzGlxM4eO1UC7UkT0F9XAuxZANhSbjFAE8byxhixQ7A9PLt8OAPhodEevnzVy8vwV/XXP2V/Zkntk5B5Dzd973vgOu3LyMaNfS1Qtb24P0hjXoxnirimpb/vSSIq7mzlU/BWcugOKxtITwI8AlgAYzsyiIxZxPEsKTP5LMp777248dWdT9J7ztZ9Pu8gYcwvGLt/m19tkRo0KcTiR74qG+GD4zahb9VpULReHUiUIx/Mv4s2XMy2VOTAWJ5+51j1fypdGUpRbioQDf9/WeADfAkhm5j7MvEiES9HDLPI3OSEeawyaynP/VVbEWpa01dYjWw+dxjM9k/yO696iBr4c2wn/96Ar1F8TLgkVFVd3UmK8vvQzljawuiQ0S2+wopEUZzdzqPgrONXFiZMSURUopR/qAzgI4F5mPm0y7iqAHermb8zcx4n5FGd8eZ16+dBU9ueex4QezTDDQxvwRKs36491WSewLuuELkyM5Jy9aMvSRNNGcvIuFMqsFpwhUt/uOACfM3MTKEF747yMu8DMKeqfCBcH8Bb5q0e6DkoFAMy863q3zz3RtYlf4WKkfaMqlsZ5K0zVu1Wi5XP5okQJQu1K1+K6quVEuISBSH3D6VB6XUP9t2+E5hHThJrUqAmRaX1boGG1cnjpHiWU35gMOHqxEvY07kNFkdS8L//+bC8AYGCbul6Pb8ye/nb/75jZ73qvY5Vju2dbZ4y5BUm1FM/QT78WUnCFGCBSAqYmM2tVhI4BqOllXBkiyiSi74mor68DEtFwdWxmbm6unXONSuxIatxzTCnI9OzKLCRNWqdrM5t/VQoVrjHxFh089QempSsRvS8PSMGSTebFs4HC2dPjVihC6q9tCwulgW3q4uAp9/G/nDynz3Ho25tsSdwUwotjAoaIPiOinSZ/6cZxrDxGvYUT11OjBQcDeFlNsjSFmd9k5jRmTqtevbp9FxKl2JHUaBapW6tiGUz7aDcyD/7uZoOZ0icZ34zrjC/HdsKANoqN5nG1uFPtSoXrvDzdvRn2TO2u5/BoTO3TAu//qAglowAzE1QjF7mCxpNrVSgyzciKE44JGGbuyswtTf5WAThORAkAoP5rmmzCzEfUfw8A2Agg1an5xhJ2RJhqx5iqaiPjeyienn/efQMAoL8aY6IJsCmrd6PDzA249aWNaDpxLQYZlkZHzlzAbU2rIrlWeQDA9+O7oFOzGkiatK5QDs997erpc29RuyKWDHeP7p3Rr6XpPi0KWKJoY4tILZFWAxiivh4CYJXnACKqTERx6utqADoA2BW2GUYhms3lz6uKcAglwlRbekxapXylL6xVso9nZLgCtF8e0MpNgAEugbPYQ+P44udT2H1MyTO6+YX1hbSjF9dlI//in9ACarW5T/vI/b+0TuWymPpf930pdStLFG2MEikBMxPAHUS0F0BXdRtElEZE89UxyQAyiWgbgA0AZjJzsRUwnjaX0V2UvJxgI0y9JTJqggcAHl+6DbM+zsb/zP8egNJrunliPN59wH3Zs+hvLgG0bEQ7vWEbAMwb7FI6W0//DA0nZOjbxrm/quY83b9gE3bl5CE9JcFrFK0TFfsEZ5Ci3zGCWVavWb8iKzAzGozPwNzBqejZMgHZx/PR45WvMH9IGh56V/ne5g1OdbOBaKwa2QETVuzQI3PHdU/C6m1H9Ar+K9Wm8q2nf+b1/MtGtMOUVTuRlZOPzRO7omr5OP36Fg5ri5nrdmNXTj62T+mGa0uVxI6jZ1AhrjSuq3ot9ueecxOOw8pmYvLkyZavXbAHW4t+C5HFm83l4Kk/goowNeYa7TmWjyeXKZXo2tSvrJ+n1w2JyHyma6HPps/7BllH8/BoJ8XePnPdHl24AMCfVwtw/4IfC33OyNkLf2LBMEULOnT6gtv1dWxSDf+6NwUAsH73CTSeuBb9Xv0OXf/9BZpOXGva2lYMv9GLCJgYwImsXrPlR8Wypd3OM+Rtd0FR21Cd7tWN+zGqc2Gn3j2vf4eso3n6Ek5jVGfX9kPvZroV2XbrKDkhQxcijxtakBhTE7SlmpRPiH5kiRQjWC0kpdknrjKjVry7duPZ18esz4+3QlPT+rbAsyuz9O2G1cvhQO55bJ10B85fvoKrVxk1KpTB6QuX3Y7/9b6TPjUarXiT53kT4uOQk6fkIs0dnIr2jarpfbIBVxJl29Pr8cTTEyTDOcxI47UiImCMQkDLIPZmczETDpogCqTSnfGcTdUWsZ58MbYTbntpI14ekOKmaWjH9Zz3sbwLaD9zA+YMTEVag8ogEG5+4XOsHNkBKXUr6XahaX1bYEDadTh36YqbQNGq1HkiNpjIIDaYIoCn52jJpt982lyMAkRbUmjBaZ6Cx1fQmr/s4eG3NsCj7yu1dTXhYgz4yzz4e6F5J1ZSUgy8FdnWlknPrszCvhPn9GWghiZcZvRr6VaxT4huRMBEMYFE6xo10bmDU7FsRHt9e/8JVx1cY37PjsNnfZ7fm3b75pe/IOtoHp7v21I/n9Em4hmkp83bX/Ems/cPzOipRxBrJUClfELs4LNcgxA5zDxHN077FAdyzyOlbiWT8a7XoxZtcTOKehqDrS6LjUWa5g5KxZPLtuHilQJ88PDNSKlbGaVLlcAzK3cqxtlq5TF2uasvkue89x8/h1qVymDP1O5udhoj3oo71a5k3gFSiH7kERClBOI50pZSRoxLCs07BCjaQbZa2d+zpYgnRkE0avEWXFQD2+5943ss36xE8pppHWbz7vfat2j3wnokTVqHDXtOFBIumnH6SgEHrJ1I0F30IgImirFaD9a4lGqhFr7OGNPRravAqx7dAdJTEnx6XgoKGMfyzGuzJKmJh6fOXUJyQjz2Te+BVSPbY+eUOzH4pnqF5q3hbakXbGb47hzl2Ha2yRXsRQRMFGNsQbJnand0TqpR6EntuZR67yElKfDyVXbTAh5VG54tfKANWiTGY9XWHK/2HOMN78noLo31dILW0z/Dvz7JRuOJa5E+71u0nPIxFv3wq9u8s6d1d5ufWexKsJnhdrfJFexHBEyUU6IE4cwffyJp0jrTJ7WVpZRRCN3atAbe8xOgZhYtqzFnvat1SHKtCvq2502uGWK16v3e5mclM9ws98i4fJOgu+hFBEwM4O9J7W8pFYg9x9sNb2xsBii2nf97SHnvyW5Nfd7kvubnb27elk9GA7T0LIpeRMBEOVae8Fa6OVq153je8Fom9S1NquHAjJ76uJS6lfUI3Vmf/OzzJvc3P19z04TrR6M7olnN8m7CVXoWRT8SyRsDGDOptR5DwfRHNksNMMNbusCMfi2Rel1lt/dGd2nstmwKthe22dy06N7xPZL0ejWA0tT+6e5KgaznnnsOwx9/SnoWhRlJFShCAsaOhvZWhYtxvOb6/mh0R/zjg63IPn4O26d0Q/nSpfzmNNmFJlwB93QBTcA+99xzkioQAawKGAm0iwFC7S4YjIDSTBzjeyS5dXJ8beN+PN09SW96BiiG6JoVyjgiZDLGdETP2cr59+eexzM9k/B8xh6vAYdCdCE6ZTEgGHeuZovRliZaZPBrG/cX+qwdHQ680TzRFQy4amQHrNx6FIAYc2MFETAxQKA3sNGtG0qB8Iwxrqr/mvZg9lmn41E0Y65W7EqMubGDLJFiAOMN/NTybZiwYid6t0o0vcnMlkMACuULWdEAPLWHCWpfI28xNlZypoJBGtDHLvI/FeUEqoGYaRPLR7QDEJw715/24ES1PTMkgzo2EQ0mynErJ+lHA/GmTZQqWSJoDcCK9rD2sVvQ45Wv0HO2ItxkCSNoiICJAazewL6EkaYBBIO/z8oSRvCGCJgYIJAbOFLaRCgCTCi6iICJEazewKJNCNGECJgiiGgTQrQgjzdBEBwjIgKGiO4hoiwiKiAir/kMRNSdiLKJaB8RjQvnHAVBCJ1IaTA7AdwF4EtvA4ioJIB5AHoAaA5gEBE1D8/0BEGwg4jYYJh5N+BeNMiEtgD2MfMBdewSAOkAdjk+QUEQbCGabTC1ARwybB9W95lCRMOJKJOIMnNzcx2fnCAI/nFMwBDRZ0S00+Qv3YnzMfObzJzGzGnVq1d34hTFBrMauIIQDI4tkZi5a4iHOAKgrmG7jrpPcBA7ilt54mRBKiG6ieb/7U0AmhBRAyIqDWAggNURnlORx+7SC07WihGin0i5qfsR0WEA7QCsIaKP1f2JRJQBAMx8BcAoAB8D2A3gA2bOisR8iwuh1I7xhvQuKt5ERMAw8wpmrsPMccxck5nvVPcfZeaehnEZzNyUmRsx8/ORmGtxwu7SC04ILCG2iOYlkhAB7GwFEq5aMUL0IrlIMYpThlO7kyWlVkzxRgRMDOKEp8eIncmSkt1dvJH/7SjHLCYl1gynUu6y+CL/41GMmYtXDKdCLCECJoox01TyL10BIIZTITYQAROl+NJUpOm7ECuIkTdK8VXAO77MNWI4FWIC+WVGMb40FTGc+keSNiOPaDBRjLh4g8dpV75gDfnFRjmiqQRHrLnyiyryqxWKHOLKjx5kiSQUOQJptys4i2gwQpFEXPnRgWgwQpFEDOTRgQgYocgiHS4jj4h1QRAcQwSMIAiOIQJGEATHEAFTzJFwesFJxMhbjJFwesFpRIMpxkg4veA0ImCKKRJOL4QDWSIVUyScXggHosEUYyScXnAa0WCKMRJOLziNCJhijoTTC04SkUcWEd1DRFlEVEBEaT7GHSSiHUS0lYgy7Tq/xH4IQniIlAazE8BdAN6wMLYzM5+068QS+yEI4SMiGgwz72bm7EicW2I/BCF8RLtVjwF8QkSbiWi4r4FENJyIMokoMzc31/xgEvshCGHFsSUSEX0GoJbJW88w8yqLh+nIzEeIqAaAT4loDzN/aTaQmd8E8CYApKWlsZc5AZDYD0EIF44JGGbuasMxjqj/niCiFQDaAjAVMFZZ+9gt6PHKV+g5W1kqSeyHIDhH1LqpiagcgBLMnK++7gZgaqjHldgPQQgfkXJT9yOiwwDaAVhDRB+r+xOJKEMdVhPA10S0DcCPANYw8zo7zi+9hgQhPEREg2HmFQBWmOw/CqCn+voAgFZhnpogCDYij3BBEBxDBIwgCI4hAkYQBMcQASMIgmOIgBEEwTFIC58vShBRLoBfIzyNagBsS9IMEzLn8FAU5lyPmav7+1CRFDDRABFlMrPXUhTRiMw5PBSnOcsSSRAExxABIwiCY4iAcY43Iz2BIJA5h4diM2exwQiC4BiiwQiC4BgiYARBcAwRMDYRQKeE7kSUTUT7iGhcOOdoMpcqRPQpEe1V/63sZdxVtbPDViJaHe55qnPw+b0RURwRLVXf/4GI6kdgmp5z8jfnoUSUa/huH4rEPA3zWUBEJ4hop5f3iYhmq9eznYhu9HtQZpY/G/4AJANoBmAjgDQvY0oC2A+gIYDSALYBaB7BOf8TwDj19TgAL3oZdy7C363f7w3AowBeV18PBLA0BuY8FMDcSM7TYz63ArgRwE4v7/cEsBYAAbgZwA/+jikajE2wtU4JbQHsY+YDzHwZwBIA6c7PzivpAN5VX78LoG/kpuITK9+b8VqWA7idtCLMkSHa/q/9wkq96999DEkHsJAVvgdQiYgSfB1TBEx4qQ3gkGH7sLovUtRk5hz19TEoVQTNKKN2bPieiPqGZ2puWPne9DHMfAXAWQBVwzI7c6z+X9+tLjeWE1Hd8EwtaAL+/UZtTd5oxKZOCWHF15yNG8zMROQtZqEeK90dGgJYT0Q7mHm/3XMthvwXwGJmvkRED0PRwLpEeE62IgImADj0TglHABifUnXUfY7ha85EdJyIEpg5R1V1T3g5htbd4QARbQSQCsW+EC6sfG/amMNEVApARQCnwjM9U/zOmZmN85sPxSYWzQT8+5UlUnjZBKAJETUgotJQjJER8cqorAYwRH09BEAhLYyIKhNRnPq6GoAOAHaFbYYKVr4347X0B7CeVctkhPA7Zw/7RR8Au8M4v2BYDeB+1Zt0M4CzhiW2OZG2XBeVPwD9oKxJLwE4DuBjdX8igAwPS/zPUDSAZyI856oAPgewF8BnAKqo+9MAzFdftwewA4oXZAeAByM010LfG5Q2Nn3U12UALAOwD0oXioZR8JvwN+cXAGSp3+0GAEkRnu9iADkA/lR/yw8CGAFghPo+AZinXs8OePGWGv8kVUAQBMeQJZIgCI4hAkYQBMcQASMIgmOIgBEEwTFEwAiC4BgSaCfYgppCsAJAMjPvIaIfAMQBqAKgLJSArJJQArUOQokuvgogVz1EW1ZydrTjERQX+jC44nMKfQaKe70LK+kBQpQhbmrBFohoKZSYn/XMPNmwfyiUeIlRHuOnQMnSnuXleL0AdGXmJ3x9hogmQ0kqfN++qxHsQpZIQsgQUXkAHaEEZg206bB/hUlksQkr1bFCFCICRrCDdADrmPlnAKeIqLUNx+wAYLOFcTsBtLHhfIIDiIAR7GAQlHonUP8dZMMxqzBzvr9BzHwVwGUiqmDDOQWbESOvEBJEVAVKiYHr1XIPJQEwEY3l0Ax8V4ioBDMXWBgbB+BiCOcSHEI0GCFU+gN4j5nrMXN9Zq4L4BcAt4R43Gwo5SZ9QkRVAZxk5j9DPJ/gACJghFAZBMU9beQ/CH2ZtAZAJwvjOqtjhShE3NRCVKLWSlnIzHf4GfchlMLlP4dnZkIgiAYjRCWsFDL6f0QU722MWshppQiX6EU0GEEQHEM0GEEQHEMEjCAIjiECRhAExxABIwiCY4iAEQTBMf4/NzaWYvGN5nkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = telecom.plot.scatter(x='T', y='VZ', figsize=(4, 4), marker='$\\u25EF$')\n",
    "ax.set_xlabel('ATT (T)')\n",
    "ax.set_ylabel('Verizon (VZ)')\n",
    "ax.axhline(0, color='grey', lw=1)\n",
    "ax.axvline(0, color='grey', lw=1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:38.363544Z",
     "iopub.status.busy": "2022-04-26T19:41:38.363347Z",
     "iopub.status.idle": "2022-04-26T19:41:38.499518Z",
     "shell.execute_reply": "2022-04-26T19:41:38.498761Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Line2D(_child2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = telecom.plot.scatter(x='T', y='VZ', figsize=(4, 4), marker='$\\u25EF$', alpha=0.5)\n",
    "ax.set_xlabel('ATT (T)')\n",
    "ax.set_ylabel('Verizon (VZ)')\n",
    "ax.axhline(0, color='grey', lw=1)\n",
    "print(ax.axvline(0, color='grey', lw=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Two or More Variables\n",
    "Load the kc_tax dataset and filter based on a variety of criteria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:38.509031Z",
     "iopub.status.busy": "2022-04-26T19:41:38.507149Z",
     "iopub.status.idle": "2022-04-26T19:41:38.722378Z",
     "shell.execute_reply": "2022-04-26T19:41:38.721537Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(432693, 3)\n"
     ]
    }
   ],
   "source": [
    "kc_tax = pd.read_csv(KC_TAX_CSV)\n",
    "kc_tax0 = kc_tax.loc[(kc_tax.TaxAssessedValue < 750000) & \n",
    "                     (kc_tax.SqFtTotLiving > 100) &\n",
    "                     (kc_tax.SqFtTotLiving < 3500), :]\n",
    "print(kc_tax0.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hexagonal binning and Contours \n",
    "### Plotting numeric versus numeric data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the number of data points gets large, scatter plots will no longer be meaningful. Here methods that visualize densities are more useful. The `hexbin` method for _pandas_ data frames is one powerful approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:38.729374Z",
     "iopub.status.busy": "2022-04-26T19:41:38.729102Z",
     "iopub.status.idle": "2022-04-26T19:41:39.130929Z",
     "shell.execute_reply": "2022-04-26T19:41:39.129237Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = kc_tax0.plot.hexbin(x='SqFtTotLiving', y='TaxAssessedValue',\n",
    "                         gridsize=30, sharex=False, figsize=(5, 4))\n",
    "ax.set_xlabel('Finished Square Feet')\n",
    "ax.set_ylabel('Tax Assessed Value')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The _seaborn_ kdeplot is a two-dimensional extension of the density plot. The calculation of the 2D-density for the full dataset takes several minutes. It is sufficient to create the visualization with a smaller sample of the dataset. With 10,000 data points, creating the graph takes only seconds. While some details may be lost, the overall shape is preserved. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:39.137141Z",
     "iopub.status.busy": "2022-04-26T19:41:39.136603Z",
     "iopub.status.idle": "2022-04-26T19:41:43.526285Z",
     "shell.execute_reply": "2022-04-26T19:41:43.525736Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4, 4))\n",
    "sns.kdeplot(data=kc_tax0.sample(10000), x='SqFtTotLiving', y='TaxAssessedValue', ax=ax)\n",
    "ax.set_xlabel('Finished Square Feet')\n",
    "ax.set_ylabel('Tax Assessed Value')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two Categorical Variables\n",
    "Load the `lc_loans` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:43.530303Z",
     "iopub.status.busy": "2022-04-26T19:41:43.529012Z",
     "iopub.status.idle": "2022-04-26T19:41:43.626716Z",
     "shell.execute_reply": "2022-04-26T19:41:43.626048Z"
    }
   },
   "outputs": [],
   "source": [
    "lc_loans = pd.read_csv(LC_LOANS_CSV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:43.630123Z",
     "iopub.status.busy": "2022-04-26T19:41:43.629381Z",
     "iopub.status.idle": "2022-04-26T19:41:43.888611Z",
     "shell.execute_reply": "2022-04-26T19:41:43.887946Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "status  Charged Off  Current  Fully Paid  Late     All\n",
      "grade                                                 \n",
      "A              1562    50051       20408   469   72490\n",
      "B              5302    93852       31160  2056  132370\n",
      "C              6023    88928       23147  2777  120875\n",
      "D              5007    53281       13681  2308   74277\n",
      "E              2842    24639        5949  1374   34804\n",
      "F              1526     8444        2328   606   12904\n",
      "G               409     1990         643   199    3241\n",
      "All           22671   321185       97316  9789  450961\n"
     ]
    }
   ],
   "source": [
    "# Table 1-8(1)\n",
    "crosstab = lc_loans.pivot_table(index='grade', columns='status', \n",
    "                                aggfunc=lambda x: len(x), margins=True)\n",
    "print(crosstab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:43.892014Z",
     "iopub.status.busy": "2022-04-26T19:41:43.891253Z",
     "iopub.status.idle": "2022-04-26T19:41:43.902378Z",
     "shell.execute_reply": "2022-04-26T19:41:43.901751Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "status  Charged Off   Current  Fully Paid      Late       All\n",
      "grade                                                        \n",
      "A          0.021548  0.690454    0.281528  0.006470  0.160746\n",
      "B          0.040054  0.709013    0.235401  0.015532  0.293529\n",
      "C          0.049828  0.735702    0.191495  0.022974  0.268039\n",
      "D          0.067410  0.717328    0.184189  0.031073  0.164708\n",
      "E          0.081657  0.707936    0.170929  0.039478  0.077177\n",
      "F          0.118258  0.654371    0.180409  0.046962  0.028614\n",
      "G          0.126196  0.614008    0.198396  0.061401  0.007187\n"
     ]
    }
   ],
   "source": [
    "# Table 1-8(2)\n",
    "df = crosstab.copy().loc['A':'G',:]\n",
    "df.loc[:,'Charged Off':'Late'] = df.loc[:,'Charged Off':'Late'].div(df['All'], axis=0)\n",
    "df['All'] = df['All'] / sum(df['All'])\n",
    "perc_crosstab = df\n",
    "print(perc_crosstab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical and Numeric Data\n",
    "_Pandas_ boxplots of a column can be grouped by a different column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:43.907303Z",
     "iopub.status.busy": "2022-04-26T19:41:43.906520Z",
     "iopub.status.idle": "2022-04-26T19:41:44.102351Z",
     "shell.execute_reply": "2022-04-26T19:41:44.101632Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "airline_stats = pd.read_csv(AIRLINE_STATS_CSV)\n",
    "airline_stats.head()\n",
    "ax = airline_stats.boxplot(by='airline', column='pct_carrier_delay',\n",
    "                           figsize=(5, 5))\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Daily % of Delayed Flights')\n",
    "plt.suptitle('')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Pandas_ also supports a variation of boxplots called _violinplot_. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:44.106286Z",
     "iopub.status.busy": "2022-04-26T19:41:44.105258Z",
     "iopub.status.idle": "2022-04-26T19:41:44.289049Z",
     "shell.execute_reply": "2022-04-26T19:41:44.288573Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "sns.violinplot(data=airline_stats, x='airline', y='pct_carrier_delay',\n",
    "               ax=ax, inner='quartile', color='white')\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Daily % of Delayed Flights')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Multiple Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:41:44.292201Z",
     "iopub.status.busy": "2022-04-26T19:41:44.291815Z",
     "iopub.status.idle": "2022-04-26T19:41:44.783055Z",
     "shell.execute_reply": "2022-04-26T19:41:44.782390Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "zip_codes = [98188, 98105, 98108, 98126]\n",
    "kc_tax_zip = kc_tax0.loc[kc_tax0.ZipCode.isin(zip_codes),:]\n",
    "kc_tax_zip\n",
    "\n",
    "def hexbin(x, y, color, **kwargs):\n",
    "    cmap = sns.light_palette(color, as_cmap=True)\n",
    "    plt.hexbin(x, y, gridsize=25, cmap=cmap, **kwargs)\n",
    "\n",
    "g = sns.FacetGrid(kc_tax_zip, col='ZipCode', col_wrap=2)\n",
    "g.map(hexbin, 'SqFtTotLiving', 'TaxAssessedValue', \n",
    "      extent=[0, 3500, 0, 700000])\n",
    "g.set_axis_labels('Finished Square Feet', 'Tax Assessed Value')\n",
    "g.set_titles('Zip code {col_name:.0f}')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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